Biomarker based classification and clustering of Veterans with PTSD

基于生物标志物的 PTSD 退伍军人分类和聚类

• 批准号: 10579692
• 负责人: ALAN N SIMMONS
• 金额: --
• 依托单位: VA SAN DIEGO HEALTHCARE SYSTEM
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10579692
• 关键词: Acceleration; Address; Algorithms; Amygdaloid structure; Behavioral; Biological; Biological Markers; Brain; Caring; Chromosome Mapping; Classification; Clinical; Cognition; Collection; Complex; DNA analysis; Data; Data Set; Development; Diagnosis; Diagnostic and Statistical Manual of Mental Disorders; Dimensions; Disease; Evaluation; Formulation; Foundations; Gene Combinations; Genetic; Grant; Graph; Grouping; Heterogeneity; Individual; Lead; Link; Literature; Machine Learning; Maps; Measurement; Measures; Methodology; Methods; Modeling; Neurobiology; Persons; Population; Post-Traumatic Stress Disorders; Reporting; Research; Sampling; Subgroup; Symptoms; Techniques; Testing; Training; Trees; Validation; Veterans; Work; advanced analytics; analytical tool; biological adaptation to stress; clinical care; comorbidity; cost; data repository; deep learning model; density; experience; feature selection; genetic analysis; genetic predictors; genome wide association study; improved; individualized medicine; military veteran; neuroimaging; neuroimaging marker; novel; personalized medicine; polygenic risk score; programs; repository; research and development; response; self organization; success; therapy development; traumatic event; traumatic stress

Posttraumatic Stress Disorder (PTSD) is both impactful on a societal and individual basis. Better treatments are needed for our Veterans, and methodological advancements can facilitate progress in the understanding and treatment of PTSD. With the dramatically reduced cost of collection and analysis of DNA there is now many large repositories that contain relatively comprehensive genetic data. Furthermore, on the individual level, many Veterans have genetic data available through commercial (e.g., 23 and Me) or research (e.g., the Million Veteran Program) entities. It is possible that access to their own genetic data will allow people to leverage the latest research developments in the aims of receiving state-of-the-art personalized medicine. New algorithmic advances can allow this data to be utilized for improving our understanding of conditions, such as PTSD. Machine learning provides a way to better identify those with PTSD through methods such as boosted trees or deep learning models. Clustering techniques can provide a way to clarify homogenous subgroups within Veteran samples. This project looks to take a supervised (aka, classification) and unsupervised (aka, clustering) analytical approach to better understand PTSD using genetic and neuroimaging data from publicly available data repositories. For this work, features are selected based on the latest advancements in the literature and models are selected through rigorous empirical evaluation. Treatment for PTSD, and other disorders, is potentially limited by classifications that are based on symptom reports such as used in DSM. This traditional approach creates biologically heterogeneous samples. We propose applying advanced analytical tools to empirically derived biomarkers to create homogeneous biologically-based groupings.

创伤后应激障碍（PTSD）对社会和 个人基础。我们的退伍军人需要更好的治疗， 这些进展可以促进对PTSD的理解和治疗的进展。 随着DNA收集和分析成本的大幅降低， 现在有很多大型的数据库，包含了比较全面的基因数据。 此外，在个人层面上，许多退伍军人有可用的遗传数据 通过商业（例如，23和我）或研究（例如，百万退伍军人计划） 实体.获取自己的基因数据可能会让人们利用 最新的研究进展，目的是接受最先进的个性化 药新的算法进步可以使这些数据用于改善 我们对创伤后应激障碍的理解机器学习提供了一种方法， 通过提升树或深度学习等方法更好地识别PTSD患者 模型聚类技术可以提供一种澄清同质子群的方法 在退伍军人样本中。 这个项目看起来采取监督（又名，分类）和无监督 (aka，聚类）分析方法，以更好地了解创伤后应激障碍使用遗传和 神经成像数据来自公开可用的数据存储库。对于这项工作，功能是 根据文献和模型的最新进展进行选择 通过严格的经验评估。治疗创伤后应激障碍和其他疾病， 潜在地受到基于症状报告的分类的限制， DSM。这种传统的方法会产生生物学上异质的样品。我们 我建议将先进的分析工具应用于经验得出的生物标志物， 基于生物学的同质分组。